# A tibble: 6 × 6,701
Diet Source Donor CollectionMet Sex OTU0 OTU1 OTU2 OTU3
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 0 0 0 0 1.56e-11 4.72e-11 1.23e-11 4.52e-11
2 0 1 0 0 0 2.36e-11 9.53e-11 3.33e-11 2.67e-11
3 0 2 0 1 0 6.77e-11 3.68e-11 8.02e-11 5.49e-11
4 0 2 0 0 0 5.52e-11 9.89e-11 4.58e-11 3.54e-11
5 0 3 0 0 0 5.24e-11 6.34e-11 2.35e-11 7.47e-11
6 0 4 0 1 0 7.67e-11 7.22e-11 5.41e-11 1.20e-11
# ℹ 6,692 more variables: OTU4 <dbl>, OTU5 <dbl>, OTU6 <dbl>, OTU7 <dbl>,
# OTU8 <dbl>, OTU9 <dbl>, OTU10 <dbl>, OTU11 <dbl>, OTU12 <dbl>, OTU13 <dbl>,
# OTU14 <dbl>, OTU15 <dbl>, OTU16 <dbl>, OTU17 <dbl>, OTU18 <dbl>,
# OTU19 <dbl>, OTU20 <dbl>, OTU21 <dbl>, OTU22 <dbl>, OTU23 <dbl>,
# OTU24 <dbl>, OTU25 <dbl>, OTU26 <dbl>, OTU27 <dbl>, OTU28 <dbl>,
# OTU29 <dbl>, OTU30 <dbl>, OTU31 <dbl>, OTU32 <dbl>, OTU33 <dbl>,
# OTU34 <dbl>, OTU35 <dbl>, OTU36 <dbl>, OTU37 <dbl>, OTU38 <dbl>, …Impact of Dietary Shifts on Gut Microbiome Dynamics
Multivariate Insights Using R
R for Bio Data Analysis
2024-12-03
Introduction
Our aim:
To study the relationship between the composition of the gut microbiota and factors such as diet and colonisation history.
General Workflow
MICROBIOME METADATA:
OTU TAXONOMY GLOSSARY:
OTU.ID Kingdom Phylum Class Order
1 OTU0 Bacteria
2 OTU1 Bacteria Firmicutes Clostridia Clostridiales
3 OTU2 Bacteria Firmicutes Bacilli Lactobacillales
4 OTU3 Bacteria Bacteroidetes Bacteroidetes Bacteroidales
5 OTU4 Bacteria Bacteroidetes
6 OTU5 Bacteria Firmicutes Clostridia Clostridiales
Family Genus X X.1
1
2 Ruminococcaceae
3 Enterococcaceae Enterococcus
4 Porphyromonadaceae Parabacteroides
5
6 Data Tidying and Filtering
Added a SampleID column to uniquely identify each sample.
Transformed the dataset from wide to long format for easier analysis.
Keeping OTUs contributing up to 95% of cumulative abundance.
Replaced the numeric codes with descriptive labels.
# Creation and relocation of SampleID
metadata_df <- metadata_df |>
mutate(SampleID = row_number()) |> # Create SampleID from the first column
relocate(SampleID,
.before = everything()) # Move SampleID to the first position
metadata_df_long <- metadata_df |>
pivot_longer(
cols = starts_with("OTU"),
names_to = "OTU",
values_to = "rel_abundance"
)
head(metadata_df_long)
# Calculate cumulative contribution
cumulative_otus <- metadata_df_long |>
group_by(OTU) |>
summarize(mean_abundance = mean(rel_abundance)) |>
arrange(desc(mean_abundance)) |>
mutate(cumulative_abundance = cumsum(mean_abundance) / sum(mean_abundance))
# Filter OTUs contributing to 95% cumulative abundance
otus_to_keep <- cumulative_otus |>
filter(cumulative_abundance <= 0.95) |>
pull(OTU)
# Number of OTUs before filtering
n_total_otus <- metadata_df_long |>
pull(OTU) |>
n_distinct()
# Number of OTUs after filtering
n_filtered_otus <- filtered_metadata |>
pull(OTU) |>
n_distinct()
filtered_metadata_stricter_label <- filtered_metadata_stricter |>
mutate(Diet = case_when(Diet == 0 ~ "LFPP",
Diet == 1 ~ "Western",
Diet == 2 ~ "CARBR",
Diet == 3 ~ "FATR",
Diet == 4 ~ "Suckling",
Diet == 5 ~ "Human")) |>
mutate(Source = case_when(Source == 0 ~ "Cecum1",
Source == 1 ~ "Cecum2",
Source == 2 ~ "Colon1",
Source == 3 ~ "Colon2",
Source == 4 ~ "Feces",
Source == 5 ~ "SI1",
Source == 6 ~ "SI13",
Source == 7 ~ "SI15",
Source == 8 ~ "SI2",
Source == 9 ~ "SI5",
Source == 10 ~ "SI9",
Source == 11 ~ "Stomach",
Source == 12 ~ "Cecum")) |>
mutate(Donor = case_when(Donor == 0 ~ "HMouseLFPP",
Donor == 1 ~ "CONVR",
Donor == 2 ~ "Human",
Donor == 3 ~ "Fresh",
Donor == 4 ~ "Frozen",
Donor == 5 ~ "HMouseWestern",
Donor == 6 ~ "CONVD")) |>
mutate(CollectionMet = case_when(CollectionMet == 0 ~ "Contents",
CollectionMet == 1 ~ "Scraping")) |>
mutate(Sex = case_when(Sex == 0 ~ "Male",
Sex == 1 ~ "Female"))
head(filtered_metadata_stricter_label)
Microbiota composition in terms of phyla in different:
Sources and Diet Types
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Diet and Donor Combination
![]()
Principal Component Analysis on Phylum-Level Aggregated Microbiome Data
- The PCA variance table shows that PC1 and PC2 together explain most of the total variance (~60%). Including PC3 and PC4 increases the cumulative explained variance to 90%, capturing most of the dataset’s variability
Western diet correlates with a negative PC1 coordinate, which aligns with Western diet observations found on the left of the previous scatter plot.
Unclassified phyla present an opposite behavior to the diet variable.
Bacteroidetes and Firmicutes also exhibit opposite behaviors, consistent with their biological significance.
Principal Component Analysis on Phylum-Level Aggregated Microbiome Data
The clear separation between green and pink points indicates that the microbiome composition is strongly influenced by diet.
Samples from the Western diet have distinct characteristics compared to those from the LFPP diet, as reflected in their separation along the principal components.
Analysis of Microbiome Clusters by Donor Groups Using Hierarchical Clustering
Pearson's Chi-squared test
data: donor_cluster_table
X-squared = 659.91, df = 8, p-value < 2.2e-16Cluster 1 is dominated by HMouseLFPP (55.5%) with notable contributions from Frozen (17.8%) and Fresh (18.7%), reflecting plant-rich diets and preserved samples.
Cluster 2 includes mostly Fresh (55.1%) and HMouseLFPP (26.5%), indicating a mix of human-derived and dietary influences.
Cluster 3 is almost entirely CONVR (95%), representing natural microbiota from control mice.
The chi-squared test confirms significant associations between donor origins and clusters, highlighting the influence of donors on microbiota composition.
Biodiversity and diet
Shannon diversity index
- Number of species living in a habitat (richness)
- Relative abundance (evenness).
\[ H' = -\sum_{i=1}^R p_i \ln p_i \\ p_i \text{ is the relative abundance of OTU}_i\\ R \text{ is the total number of OTUs} \]
Biodiversity in the microbiota of first-generation humanized mice was found to differ significantly across different diets.